Predicting ADAS13
Here we will diagnose ADAS13
Learning ADAS13
BESSml <- BESS(ADAS13~.,TADPOLECrossMRITrain)
pander::pander(t(BESSml$selectedfeatures))
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| Gender |
Hippocampus |
ST128SV |
ST1SV |
ST2SV |
ST68SV |
M_ST24TA |
M_ST25TA |
M_ST44TA |
M_ST54TA |
M_ST57TA |
M_ST62TA |
M_ST13TS |
M_ST15TS |
M_ST23TS |
M_ST25TS |
M_ST39TS |
M_ST44TS |
M_ST48TS |
M_ST54TS |
M_ST56TS |
M_ST14SA |
M_ST15SA |
M_ST23SA |
M_ST24SA |
M_ST31SA |
M_ST129SA |
M_ST35SA |
M_ST39SA |
M_ST47SA |
M_ST50SA |
M_ST51SA |
M_ST55SA |
M_ST57SA |
M_ST40CV |
M_ST45CV |
M_ST46CV |
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| M_ST52CV |
M_ST60CV |
M_ST17SV |
M_ST30SV |
M_ST61SV |
M_ST65SV |
RD_ST13TA |
RD_ST15TA |
RD_ST23TA |
RD_ST31TA |
RD_ST32TA |
RD_ST35TA |
RD_ST43TA |
RD_ST44TA |
RD_ST46TA |
RD_ST49TA |
RD_ST50TA |
RD_ST55TA |
RD_ST56TA |
RD_ST60TA |
RD_ST62TA |
RD_ST13TS |
RD_ST14TS |
RD_ST24TS |
RD_ST32TS |
RD_ST40TS |
RD_ST43TS |
RD_ST46TS |
RD_ST47TS |
RD_ST48TS |
RD_ST50TS |
RD_ST57TS |
RD_ST59TS |
Table continues below
| RD_ST60TS |
RD_ST62TS |
RD_ST15SA |
RD_ST24SA |
RD_ST32SA |
RD_ST129SA |
RD_ST35SA |
RD_ST38SA |
RD_ST44SA |
RD_ST47SA |
RD_ST50SA |
RD_ST57SA |
RD_ST58SA |
RD_ST59SA |
RD_ST60SA |
RD_ST14CV |
RD_ST15CV |
RD_ST24CV |
RD_ST25CV |
RD_ST26CV |
RD_ST32CV |
RD_ST34CV |
RD_ST35CV |
RD_ST36CV |
RD_ST40CV |
RD_ST44CV |
RD_ST46CV |
RD_ST52CV |
RD_ST54CV |
RD_ST56CV |
RD_ST62CV |
RD_ST16SV |
RD_ST18SV |
| RD_ST21SV |
RD_ST61SV |
RD_ST65SV |
RD_ST66SV |
prreg <- predictionStats_regression(cbind(TADPOLECrossMRITest$ADAS13,predict(BESSml,TADPOLECrossMRITest)),"ADAS13")
ADAS13

pander::pander(prreg)
corci:
biasci: -0.4464, -0.9868 and
0.0941
RMSEci: 7.32, 6.95 and
7.72
spearmanci:
MAEci:
pearson:
Pearson’s product-moment correlation:
predictions[, 1] and
predictions[, 2]
| 23.3 |
702 |
1.13e-89 * * * |
two.sided |
0.661 |
par(op)
Diagnosis MCI vs
AD
Learning
#TADPOLE_DX_TRAIN$DX <- as.factor(TADPOLE_DX_TRAIN$DX)
BESSDXml <- BESS(DX~.,TADPOLE_DX_TRAIN)
pr <- predict(BESSDXml,TADPOLE_DX_TEST,type="response")
prBin <- predictionStats_binary(cbind(TADPOLE_DX_TEST$DX,pr),"MCI vs Dementia")
MCI vs Dementia

pander::pander(prBin$aucs)
pander::pander(prBin$accc)
pander::pander(prBin$berror)
pander::pander(prBin$sensitivity)
par(op)
Diagnosis NL vs AD
Learning
BESSDXmlNLDE <- BESS(DX~.,TADPOLE_DX_NLDE_TRAIN)
prBin <- predictionStats_binary(cbind(TADPOLE_DX_NLDE_TEST$DX,predict(BESSDXmlNLDE,TADPOLE_DX_NLDE_TEST,type="response")),"NL vs Dementia")
NL vs Dementia

pander::pander(prBin$aucs)
pander::pander(prBin$accc)
pander::pander(prBin$berror)
pander::pander(prBin$sensitivity)
par(op)
Prognosis MCI to AD
Conversion
Cox modeling of MCI to Dementia Conversion
Learning
Learning COX and ploting performance
bConvml <- BESS(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)
ptestl <- predict(bConvml,TADPOLE_Conv_TEST,type="response")
ptestr <- exp(ptestl)
boxplot(ptestl~TADPOLE_Conv_TEST$status)

boxplot(ptestr~TADPOLE_Conv_TEST$status)

perdsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
TADPOLE_Conv_TEST$status,
ptestl,
ptestr)
if (max(ptestl)>0 && min(ptestl)<0 )
{
prSurv <- predictionStats_survival(perdsurv,"MCI to AD Conversion")
pander::pander(prSurv$CIRisk)
pander::pander(prSurv$CILp)
pander::pander(prSurv$spearmanCI)
}